2,418 research outputs found
Production and characterization of an orally immunogenic Plasmodium antigen in plants using a virus-based expression system RID F-7326-2010
No full textIncreasing numbers of plant-made vaccines and pharmaceuticals are entering the
late stage of product development and commercialization. Despite the theoretical
benefits of such production, expression of parasite antigens in plants, particularly
those from Plasmodium, the causative parasites for malaria, have achieved only limited
success. We have previously shown that stable transformation of tobacco plants
with a plant-codon optimized form of the Plasmodium yoelii merozoite surface protein
4 ⁄ 5 (PyMSP4 ⁄ 5) gene resulted in PyMSP4 ⁄ 5 expression of up to 0.25% of
total soluble protein. In this report, we describe the rapid expression of PyMSP4 ⁄5 in
Nicotiana benthamiana leaves using the deconstructed tobacco mosaic virus-based
magnICON expression system. PyMSP4 ⁄ 5 yields of up to 10% TSP or 1–2 mg⁄g of
fresh weight were consistently achieved. Characterization of the recombinant plantmade
PyMSP4 ⁄ 5 indicates that it is structurally similar to PyMSP4 ⁄ 5 expressed by
Escherichia coli. It is notable that the plant-made PyMSP4 ⁄ 5 protein retained its
immunogenicity following long-term storage at ambient temperature within freezedried
leaves. With assistance from a mucosal adjuvant the PyMSP4 ⁄ 5-containing
leaves induced PyMSP4 ⁄ 5-specific antibodies when delivered orally to naı ̈ve mice or
mice primed by a DNA vaccine. This study provides evidence that immunogenic
Plasmodium antigens can be produced in large quantities in plants using the
magnICON viral vector system.
Introduction
Malaria is a major world health problem caused by species
of Plasmodium, a protozoan parasite. Development of vaccines
targeting various stages of the parasite life cycle, in
combination with currently available control measures,
appears to be necessary for the eventual elimination of
this disease. Owing to the relative poverty and lack of
infrastructure in many malaria-endemic areas, a successful
immunization strategy will have more probability of success
if i
Murine models of primary biliary cirrhosis: Comparisons and contrasts
No full textOertelt S, Ridgway WM, Ansari AA, Coppel RL, Gershwin ME. Murine models of primary biliary cirrhosis: Comparisons and contrasts. Hepatology Research. 2007;37(s3):S365-S369
Teacher-apprentices RL (TARL): leveraging complex policy distribution through generative adversarial hypernetwork in reinforcement learning
No full textTypically, a Reinforcement Learning (RL) algorithm focuses in learning a single deployable policy as the end product. Depending on the initialization methods and seed randomization, learning a single policy could possibly leads to convergence to different local optima across different runs, especially when the algorithm is sensitive to hyper-parameter tuning. Motivated by the capability of Generative Adversarial Networks (GANs) in learning complex data manifold, the adversarial training procedure could be utilized to learn a population of good-performing policies instead. We extend the teacher-student methodology observed in the Knowledge Distillation field in typical deep neural network prediction tasks to RL paradigm. Instead of learning a single compressed student network, an adversarially-trained generative model (hypernetwork) is learned to output network weights of a population of good-performing policy networks, representing a school of apprentices. Our proposed framework, named Teacher-Apprentices RL (TARL), is modular and could be used in conjunction with many existing RL algorithms. We illustrate the performance gain and improved robustness by combining TARL with various types of RL algorithms, including direct policy search Cross-Entropy Method, Q-learning, Actor-Critic, and policy gradient-based methods.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive Intelligenc
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
No full textWhile reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) provides a principled answer to both state estimation and the exploration-exploitation trade-off, but struggles to scale. To tackle this challenge, BRL frameworks with various prior assumptions have been proposed, with varied success. This work presents a representation-agnostic formulation of BRL under partially observability, unifying the previous models under one theoretical umbrella. To demonstrate its practical significance we also propose a novel derivation, Bayes-Adaptive Deep Dropout rl (BADDr), based on dropout networks. Under this parameterization, in contrast to previous work, the belief over the state and dynamics is a more scalable inference problem. We choose actions through Monte-Carlo tree search and empirically show that our method is competitive with state-of-the-art BRL methods on small domains while being able to solve much larger ones.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive Intelligenc
Refined Risk Management in Safe Reinforcement Learning with a Distributional Safety Critic
No full textSafety is critical to broadening the real-world use of reinforcement learning (RL). Modeling the safety aspects using a safety-cost signal separate from the reward is becoming standard practice, since it avoids the problem of finding a good balance between safety and performance. However, the total safety-cost distribution of different trajectories is still largely unexplored. In this paper, we propose an actor critic method for safe RL that uses an implicit quantile network to approximate the distribution of accumulated safety-costs. Using an accurate estimate of the distribution of accumulated safetycosts, in particular of the upper tail of the distribution, greatly improves the performance of riskaverse RL agents. The empirical analysis shows that our method achieves good risk control in complex safety-constrained environments.AlgorithmicsIntelligent Electrical Power Grid
qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation
No full textCompiling a quantum circuit for specific quantum hardware is a challenging task. Moreover, current quantum computers have severe hardware limitations. To make the most use of the limited resources, the compilation process should be optimized. To improve currents methods, Reinforcement Learning (RL), a technique in which an agent interacts with an environment to learn complex policies to attain a specific goal, can be used. In this work, we present qgym, a software framework derived from the OpenAI gym, together with environments that are specifically tailored towards quantum compilation. The goal of qgym is to connect the research fields of Artificial Intelligence (AI) with quantum compilation by abstracting parts of the process that are irrelevant to either domain. It can be used to train and benchmark RL agents and algorithms in highly customizable environments.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Quantum Circuit Architectures and Technolog
Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems
No full textLearning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for deep RL to be applicable. We focus on domains where agents interact with a reduced portion of a larger environment while still being affected by the global dynamics. Our method combines the use of local simulators with learned models that mimic the influence of the global system. The experiments reveal that incorporating this idea into the deep RL workflow can considerably accelerate the training process and presents several opportunities for the future.Interactive IntelligenceAlgorithmic
Congenital Plasmodium falciparum infection in neonates in Muheza District, Tanzania.
No full textBACKGROUND\ud
\ud
Although recent reports on congenital malaria suggest that the incidence is increasing, it is difficult to determine whether the clinical disease is due to parasites acquired before delivery or as a result of contamination by maternal blood at birth. Understanding of the method of parasite acquisition is important for estimating the time incidence of congenital malaria and design of preventive measures. The aim of this study was to determine whether the first Plasmodium falciparum malaria disease in infants is due to same parasites present on the placenta at birth.\ud
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METHODS\ud
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Babies born to mothers with P. falciparum parasites on the placenta detected by PCR were followed up to two years and observed for malaria episodes. Paired placental and infant peripheral blood samples at first malaria episode within first three months of life were genotyped (msp2) to determine genetic relatedness. Selected amplifications from nested PCR were sequenced and compared between pairs.\ud
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RESULTS\ud
\ud
Eighteen (19.1%) out of 95 infants who were followed up developed clinical malaria within the first three months of age. Eight pairs (60%) out of 14 pairs of sequenced placental and cord samples were genetically related while six (40%) were genetically unrelated. One pair (14.3%) out of seven pairs of sequenced placental and infants samples were genetically related. In addition, infants born from primigravidae mothers were more likely to be infected with P. falciparum (P < 0.001) as compared to infants from secundigravidae and multigravidae mothers during the two years of follow up. Infants from multigravidae mothers got the first P. falciparum infection earlier than those from secundigravidae and primigravidae mothers (RR = 1.43).\ud
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CONCLUSION\ud
\ud
Plasmodium falciparum malaria parasites present on the placenta as detected by PCR are more likely to result in clinical disease (congenital malaria) in the infant during the first three months of life. However, sequencing data seem to question the validity of this likelihood. Therefore, the relationship between placental parasites and first clinical disease need to be confirmed in larger studies
Anti-mitochondrial antibodies and primary biliary cirrhosis in TGF-b receptor II dominant-negative mice.
No full textAMA production in primary biliary cirrhosis is promoted by the TLR9 ligand CpG and suppressed by potassium channel block
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